Diagnostic method for locating a failure in a complex system, and a device for implementing said method

ABSTRACT

A diagnostic method for locating a failure in a complex system that includes the steps of detecting operating information using a detector device; verifying a performance of the system based on the operating information; determining an operating status of the system to be one of “operational,” “non-operational” and “degraded” based on the operating information; comparing the operating information with predetermined data when the operation status is one of “operational,” “non-operational” and “degraded;” and generating at least one hypothesis as to a location of the failure of the system.

Priority is claimed to French Application No. FR 08 03835, filed Jul. 7,2008, the entire disclosure of which is incorporated by referenceherein.

The present invention relates to the general technical field of methodsof diagnosing failures in complex systems. Such complex systems are tobe found in particular in aircraft, such as helicopters, where theyserve to optimize management of their operation in various flightconfigurations. For safety reasons, it is essential to identify anymalfunction in order to determine its cause and replace the faultyelement.

BACKGROUND

The term “complex systems” is used to mean systems comprising aplurality of interconnected elements, such as electrical components,electronic components, or computers. Such complex systems are to befound in various economic sectors and in particular in aviation,industry, or automation. Problems often arise during maintenanceoperations, insofar as it can be very difficult to locate a defectiveelement of the system that is giving rise to a failure.

Furthermore, implementing failure detection methods with additionaldetection equipment, is not always effective since, for reasons ofsafety, that can lead to an entire set of elements being replaced. Inany event, maintenance operations that do not enable a failure to belocated accurately, or that involve the use of additional detectionequipment, give rise to an increase in maintenance costs.

Diagnostic methods are already known for locating a failure in a complexsystem, which methods consist: in verifying the performance of thecomplex system on the basis of operating information issued by detectormeans; in responding to the operating information to determine anoperating status of the system that may be “operational”,“non-operational”, or “degraded”; and when the operating status isdetermined to be “non-operational” or “degraded”, in comparing theoperating information with predetermined information; and in generatingat least one hypothesis as to the location of the failure in the complexsystem.

Those diagnostic methods nevertheless present a certain number ofdrawbacks. Known diagnostic methods are based, amongst other things, ona probabilistic analysis of failure. Such analysis generally makes itpossible to inform the maintenance operator about one or more elementsthat might be giving rise to a failure, with some given degree ofcertainty. This degree is expressed by calculating a correspondingprobability. Known diagnostic methods use a calculation algorithm thatusually involves quasi-arbitrary approximations and weightings for thefailure messages coming from various tests. In addition, diagnosticmethods define arbitrary time windows, e.g. having a duration of tenseconds, during which the failure messages relating to distinct failuresare taken into account. The results obtained in this way by suchalgorithms for locating a failure in a complex system are therefore notsuitable for practical use.

For example, document GB 2 426 090 discloses a method of determining thetime remaining before failure for complex systems or subsystems. Themethod described is based on using statistical and probabilisticanalysis of the reliability of the monitored systems. The method makesuse firstly of determining failures on the basis of an operating datahistory relating to the monitored systems, and secondly on continuoussurveillance of said systems by means of sensors. The recordedhistorical data also makes it possible to establish causal networks foridentifying the causes of failures by implementing mathematicaldistribution functions. These functions are based on probabilitiesmaking it possible to establish to reliability and the probabilitydensity for said systems.

Diagnostic methods are also known that are based on a static failuretree, defining logical relationships between breakdown messages via acertain number of logic gates. By way of example, mention can be madeof:

-   -   the “AND” logic gate that is true when all of its 2 to n inputs        are true;    -   the “OR” logic gate that is true when at least one of its n        inputs is true;    -   the “NOT” logic gate that presents an output that is the inverse        of the input (generally in the form of a “NAND” gate or a “NOR”        gate that presents an output that is the inverse of an “AND”        logic gate or of an “OR” logic gate);    -   a “K-of-M” logic gate that is true when K out of a total of M of        its inputs are true.

A dynamic failure tree is also known from a field other than diagnosis,which tree defines logic and dynamic relationships between breakdownmessages by a certain number of additional logic gates. By way ofexample, mention can be made of:

-   -   the priority AND gate, or “PAND” gate, that is true when all of        its inputs are true in a predefined order; and    -   the functional dependency gate, or “FDEP” gate, that is true if        one specific input is true or if a set of gates is true. Thus,        when the specific input is true, all of the other inputs are        forced to take on a true state.

Nevertheless, it has been found that those logic gates do not enable adiagnosis to be obtained that is sufficiently accurate for locatingfailures in a complex system.

Known methods also generate numerous false breakdowns due to takingaccount of breakdown messages outside their context. This often leads todiagnosis being polluted, and consequently to difficulties in locatingbreakdowns and in particular to ambiguities in locations for saidbreakdowns. At the end of each flight, a very large number of pieces ofequipment are thus said to be faulty.

SUMMARY OF THE INVENTION

An aspect of the present invention is to provide a novel diagnosticmethod that makes it possible to overcome the above-mentionedlimitations and to take account of the problem as a whole in the contextof filtering breakdown messages and locating said breakdown.

Another aspect of the present invention is to obtain better accuracy inlocating a failure, while not significantly increasing the costassociated with obtaining such a location.

In another embodiment, the present invention provides a novel diagnosticmethod that can be implemented in existing complex systems, withoutrequiring alterations, in particular alterations concerning thearrangement of detector means such as sensors, and by optimizing the useof existing test means.

Another embodiment provides a novel device for providing assistance inlocating failures by implementing the above-mentioned diagnostic method.The device in accordance with the invention thus comprises tools forprocessing breakdown messages in an overall view of the system asopposed to processing said messages as such, outside a particularcontext or environment.

An embodiment of the present invention provides a diagnostic method forlocating a failure in a complex system, the method consisting: inverifying the performance of the complex system on the basis ofoperating information issued by detector means; in responding to theoperating information by determining an operating status of the systemthat is said to be “operational”, “non-operational”, or “degraded”; andwhen a “non-operational” or “degraded” operating status has beendetermined, in comparing the operating information with predetermineddata; and in generating at least one hypothesis as to the location ofthe failure in the complex system;

wherein the method comprises:

-   -   in a first step (100), triggering a diagnostic algorithm by        detecting failure occurrences within the operating information        and generating corresponding failure messages or recovering        failure messages generated directly by the complex system;    -   in a second step (200), filtering the failure messages to        eliminate erroneous failure messages by defining logical and        dynamic relationships of a dynamic failure tree that must be        satisfied by said failure messages;    -   in a third step (300), capturing the information inherent to the        failure messages that remain at the end of the preceding step;    -   in a fourth step (600), taking the failure messages from a time        window T, sorting the results obtained by the preceding step,        and comparing said results with the predetermined data by using        logical and dynamic relationships of a dynamic failure tree to        identify and locate a failure; and    -   in a fifth step (700), generating a diagnosis identifying and        locating the failure.

The dynamic failure tree makes it possible to generate rules for useduring the second step (200) and the fourth step (600), correspondingrespectively to filtering and to data analysis-and-comparison. Thedynamic failure tree thus determines logic and dynamic relationshipsbetween the failure messages, the context data such as values issued bysensors, and the duration and the rate of occurrence of the failuremessages.

In an implementation of the diagnostic method in accordance with theinvention, the third step (300) comprises:

-   -   a first stage (310) consisting in determining the times at which        failure messages appear and disappear;    -   a second stage (320) consisting in verifying whether the same        failure message appears on redundant pieces of equipment, and if        so grouping said messages together for analysis purposes; and    -   a third stage (330) consisting in determining whether dependency        exists between the failure messages that appear in a time window        T, and if there is no such dependency, in processing them        separately.

By way of example, the messages that appear are considered as relatingto the same breakdown when the time differences between the times atwhich the respective messages appear and the time differences betweenthe times at which the respective messages disappear, are below apredetermined threshold S.

If t1_(a) and t2_(a) are the respective appearance times of messages (1)and (2), and if t1_(d) and t2_(d) are the respective disappearance timesof said messages (1) and (2), then a strong correlation is obtained ift1_(a)-t2_(a) and t1_(d)-t2_(d) are below the threshold S.

In an implementation of the diagnostic method in accordance with theinvention, the third step (300) comprises:

-   -   a counting stage (340) consisting in determining the number of        failure messages that appear in the time window T; and    -   a timing stage (350) consisting in determining the durations of        the failure messages.

In an implementation, the diagnostic method in accordance with theinvention consists, in the event of ambiguity in the location of afailure at the end of the fourth step (600), in implementing anadditional step (650) consisting in performing probabilistic analysis.

In an implementation, the probabilistic analysis consists in taking thebreakdown messages generated by equipment subassemblies, in identifyingthe subassemblies that are ambiguous as to location; and then indetermining a ratio for the mean time between failures (MTBF) of saidsubassemblies, which ratio constitutes additional information on whichto base locating the failure.

For this purpose, use is made of the results of failure mode and effectsanalysis (FMEA), either at component level, or at functional blocklevel, in order to define the probability of a breakdown in each of thesubassemblies making up a piece of equipment.

When in a state where there is ambiguity concerning the location of thefailure between two pieces of equipment, in the prior art a ratio isestablished between the failure rates for each of those two pieces ofequipment as a whole. This amounts to performing first processing withmessages relating to the nature of breakdowns and then in ignoring thesemessages relating to the nature of the breakdown in the report madesubsequently. This leads to a ratio of a set of failure messages thatmight appear for each of the two pieces of equipment as a whole. Thisleads to a loss of information concerning the failing portion of a pieceof equipment, even though that information was available initially. Thediagnostic method in accordance with the invention makes it possible tomitigate that drawback.

In general when there is ambiguity about a breakdown, it comes from theinputs/outputs of the various pieces of equipment. In the context of theinvention, a ratio is taken not between the equipment failure rates, butbetween the failure rates of the various modules that are suspected offailing. For example, this might be the ratio between the reliability(or failure rate) of the output block of pieces of equipment No. 1 andthe reliability of the input block of pieces of equipment No. 2.

In an implementation of the diagnostic method in accordance with theinvention, the fourth step (600) includes an ordering stage (610)consisting in determining a chronological order for the failuremessages.

In an implementation of the diagnostic method in accordance with theinvention, the fourth step (600) includes a context stage (620)consisting in performing context correlation analysis on the failuremessages.

In an implementation of the diagnostic method in accordance with theinvention, the second step (200) includes a filter stage (210)consisting in filtering the failure messages as a function of thedurations of appearance of said messages.

It is found that about 40% of failure or breakdown messages have anappearance duration that is shorter than three seconds. Such transientbreakdowns may be due for example to poorly-defined detection thresholdsor to problems with connectors.

The breakdown detection threshold may be under-dimensioned relative tothe physical phenomenon being measured. For example, electronicequipment can detect power line disturbances of very short duration thatoccur during the normal operation of a helicopter. The durations ofthese disturbances may be longer than determined thresholds, and canthus give rise to breakdown messages.

In a given environment, a helicopter may be subjected to vibrationstresses that give rise to a large number of short disturbances incommunications, associated with connectors and not corresponding at allto breakdowns.

In an implementation of the diagnostic method in accordance with theinvention, the second step (200) consists in using a filtering andcorrelation stage (220) to filter the failure messages as a function ofa correlation between said messages and a context such as in-flight oron the ground.

By way of example, complex information may comprise:

-   -   various stages of flight, stages on the ground, and in        particular stages of switching pieces of equipment on and off;    -   pilot actions;    -   helicopter configurations; and    -   the dynamic configuration of the helicopter corresponding to the        on/off states of various pieces of equipment.

It is also appropriate to filter erroneous breakdown messages thatresults from the external environment. A helicopter may be started withthe help of a battery, giving rise to one side of the helicopterstarting followed by the other side starting. During this startingstage, observing only part of the equipment in operation will generate alarge number of breakdown messages.

In an implementation of the diagnostic method in accordance with theinvention, the second step (200) consists in using anotherfilter-and-correlation stage (230) to filter the failure messages as afunction of correlation between said failure messages and a data setcomprising the durations of appearance, the context, the generatedfailure messages, and where appropriate additional data.

More precisely, this may constitute filtering relative to an expertanalysis. This is because the complexity of implementing such filteringis such that it is often not possible to determine the correspondingrules a priori. Under such circumstances, it is imperative to definefiltering rules a posteriori, in compliance with the otherfilter-and-correlation stage (230) feeding back experience andinformation as collected on the machine, and with the help of experts.

By way of example, filtering rules may be generated by a genericconstruction. Such a construction consists in filtering the breakdowninformation on the basis of certain breakdown characteristics. By way ofexample, if a breakdown occurs on the ground and disappears in flightand if the breakdown is not considered to be critical, then saidbreakdown is filtered. This type of construction is simple to implementbefore developing a helicopter.

Filtering rules may also be generated by a specific construction. Such aconstruction consists in filtering specific breakdown information as afunction of breakdown messages. Such construction is based on feedingback experience and on expert analysis. By way of example, if breakdownmessage No. 1 appears and the flight stage corresponds to hovering, thenthe breakdown message is filtered.

In an implementation, the diagnostic method consists in defining rulesin the context of a dynamic failure tree with the help of logic anddynamic gates, comprising in particular:

-   -   a “NUMBER OF OCCURRENCES” gate that is true when the number of        breakdown messages recorded in flight is greater than or less        than a defined threshold;    -   a “DURATION” gate that is true when the duration of appearance        of a breakdown message is greater than or less than a defined        threshold;    -   an above-mentioned “PAND” gate; and    -   a “timed PAND” gate that is true when its inputs are true and        appear in a determined order with the rate of appearance being        greater than or less than a determined threshold.

The “NUMBER OF OCCURRENCES” gate may be used for example to filterfailure messages, and the “DURATION” gate may be used for example totake account of the durations of failure messages and also to filterthem.

Taking account of the rate at which failure messages appear reliesmainly on the “PAND” and “timed PAND” gates. These may also be used inassociation with known gates such as AND, OR, NAND, NOR, and K-of-Mgates.

The invention thus makes it possible to add dependencies between thebreakdown messages and to ensure that these dependencies are applied ata determined rate. It then becomes possible to take account firstly ofthe dependency between different failure messages and secondly of thechronological order of said messages.

In addition, the phenomenon of the effects of a breakdown probabilityoften gives rise to a large number of failure messages appearing thatdepend on the architecture of the system in which the breakdown hasoccurred. The appearance of a breakdown at a specific location producesa cascade of effects on pieces of equipment downstream from saidspecific location, these breakdowns themselves giving rise to failuremessages. Taking account of the rate at which these failure messagesappear enables the source of the breakdown to be isolated.

The “timed PAND” gate used, e.g. in the second step (200) and in thefourth step (600), thus presents a time threshold in addition to adetermined order of appearance. The inputs to this “timed PAND” gatemust therefore be true in a defined order and they must also be true fora length of time that is longer than or shorter than the predefined timethreshold.

The embodiments of the present invention may also be achieved with thehelp of a device for assisting in locating a failure in a complex systemby implementing the diagnostic method as described above and comprising:

-   -   detector means arranged in a complex system and delivering        operating information of said complex system;    -   a central unit comprising: storage means for storing        predetermined data; filter means for filtering operating        information and/or associated failure messages; and sorting,        analysis, and comparison means acting on the predetermined data        and the operating information to generate and issue a failure        location message;    -   presentation means for presenting the location message generated        by the central unit; and    -   recording means incorporated in the central unit to store        firstly the operating information giving rise to a failure        message and secondly the messages that appear in the        presentation means.

In an embodiment in accordance with the invention, the central unitincludes means for defining time windows during which failureoccurrences must appear in successive in order to be taken into account.

The embodiments of the present invention may also be achieved with thehelp of an aircraft including at least one device as described above.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention and its advantages appear in greater detail from thefollowing description of an implementation given by way of illustrationwith reference to the accompanying figures, in which:

FIG. 1 is a flow chart showing the step of an implementation of thediagnostic method in accordance with the invention;

FIG. 2 is a more detailed diagram of the information capture step of animplementation of the diagnostic method in accordance with the inventionas shown in FIG. 1;

FIG. 3 is a more detailed diagram of the data comparison-and-analysisstep for an implementation of the diagnostic method in accordance withthe invention;

FIG. 4 is a diagram of an embodiment of a device in accordance with theinvention, enabling the diagnostic method in accordance with theinvention to be implemented; and

FIG. 5 is a more detailed diagram of the filter step of animplementation of the diagnostic method in accordance with theinvention, as shown in FIG. 1.

DETAILED DESCRIPTION

Any elements that are structurally and functionally identical and thatare shown in more than one of the figures, are given the same referencesin each of them.

FIG. 1 shows an implementation of the diagnostic method in accordancewith the invention.

The first step (100) consists in triggering a diagnostic algorithm fordetecting occurrences of failure within the operating information, andfor generating corresponding failure messages. The first step (100) mayalso consist in recovering failure messages generated directly by thecomplex system, such as an aircraft or pieces of equipment of saidaircraft.

The second step (200) consists in filtering the failure messages inorder to eliminate erroneous messages. This elimination is achieved bymeans of a dynamic failure tree DFT defining logical and dynamicrelationships that must be satisfied by the failure messages.

In the second step (200), the diagnostic method consists in a filterstage (210) of filtering failure messages as a function of the durationof appearance of said messages.

In a filter-and-correlation stage (220) of the second step (200), thediagnostic method consists in filtering the failure messages as afunction of correlation between said messages and a context such as aflight stage or a ground stage.

In another filter-and-correlation stage (230) of the second step (200),the diagnostic method consists in filtering the failure messages as afunction of correlation between said messages and a data set comprisingthe duration of appearance, the context, the generated failure messages,and where appropriate, additional data.

The third step (300) consists in capturing the information inherent tothe failure messages that remain at the end of filtering. FIG. 2 showsan implementation of the third step (300). As shown in FIG. 2, this stepcomprises, for example, a succession of one or more of the followingstages:

-   -   a first stage (310) consisting in determining the times at which        failure messages appear and disappear;    -   a second stage (320) consisting in verifying whether the same        failure message appears on redundant pieces of equipment, and if        so of grouping said messages together for analysis purposes;    -   a third stage (330) consisting in determining whether dependency        exists between the failure messages in a time window T, and if        there is no such dependency, in processing them separately;    -   a counting stage (340) consisting in determining the number of        failure messages that appear in the time window T; and    -   a timing stage (350) consisting in determining the durations of        the failure messages.

The fourth step (600) consists in sorting and/or analyzing the remainingfailure messages that appear in a given predefined time window T, and/orin comparing them with prerecorded data by means of a dynamic failuretree DFT in order to identify and locate a breakdown. By way of example,the fourth step (600), as shown in FIG. 3, comprises stages includingthe following:

-   -   an ordering stage (610) consisting in putting the failure        messages into chronological order; and    -   a context stage (620) consisting in analyzing contextual        correlation between failure messages.

In an implementation, in the event of ambiguity in locating a failure atthe end of the fourth step (600) the diagnostic method in accordancewith the invention consists in implementing an additional step (650)that consists in performing probabilistic analysis.

The fifth step (700) then consists in generating a correspondingdiagnosis. Depending on the circumstances, the diagnostic methodimplements the additional step (650) that consists in performing aprobabilistic analysis at the end of the fourth step (600), assumingthat the breakdown could not be located sufficiently accurately.

FIG. 4 is a diagram of an embodiment of a device 1 in accordance withthe invention for assisting in locating a breakdown. This device 1serves to implement the diagnostic method in accordance with theinvention. The device 1 has detector means 2 arranged in the complexsystem and delivering operating information about said complex system.The device 1 also includes a central unit 3 including storage means 4for storing predetermined data, filter means 5 for filtering operatinginformation and/or associated failure messages, and sorting, analysis,and comparative means 6 acting on the predetermined data and theoperating information in order to generate and issue a failure locationmessage. The device 1 in accordance with the invention also includesdiagnosis presentation means 7, and more particularly means forpresenting the location message generated by the central unit 6.

In an embodiment in accordance with the invention, the device 1 includesrecording means 8, e.g. incorporated in the central unit 3, for storingboth the operating information from which failure messages are derivedand the messages that appear on the presentation means 7. The centralunit 3 also includes means for defining time windows T, e.g. of durationshorter than 3 seconds, during which failure occurrences must appear insuccession in order to be taken into account. By way of example,filtering relative to a three-second time window T is implemented in thecontext of step 200. The architecture of the central unit 3 is arrangedabout a microprocessor, for example.

Naturally, the present invention can be implemented in numerousvariations. Although several embodiments and implementations aredescribed, it will be readily be understood that it is not conceivableto identify exhaustively all possible implementations. It is Naturallypossible to envisage replacing any of the means described by equivalentmeans, or any of the steps described by an equivalent step, withoutgoing beyond the ambit of the present invention.

1. A diagnostic method for locating a failure in a complex system, themethod comprising: detecting operating information using a detectordevice; verifying a performance of the system based on the operatinginformation; determining an operating status of the system to be one of“operational,” “non-operational” and “degraded” based on the operatinginformation; comparing the operating information with predetermined datawhen the operation status is one of “operational,” “non-operational” and“degraded;” and generating at least one hypothesis as to a location ofthe failure of the system, wherein the generating includes: in a firststep, triggering a diagnostic algorithm by detecting a failureoccurrence within the operating information and generating correspondingfailure messages or recovering failure messages generated directly bythe system; in a second step, filtering the failure messages so as toeliminate erroneous failure messages by defining logical and dynamicrelationships of a dynamic failure tree (DFT) that must be satisfied bythe failure message; in a third step, capturing information inherent tothe failure messages not eliminated in the second step; in a fourthstep, taking the failure message from a time window T, sorting thecaptured information so as to obtain results, and comparing the resultswith predetermined data using the DFT so as to identify and locate afailure; and in a fifth step, generating a diagnosis identifying andlocating the failure.
 2. The diagnostic method recited in claim 1,wherein the third step includes: in a first stage, determining a time atwhich failure messages appear and disappear; in a second stage,verifying whether the same failure messages appear on redundant piecesof equipment, and if so grouping the same failure messages together foranaylsis; and in a third stage, determining whether dependency existsbetween failure messages appearing within time window T and processingthe failure messages separately if no dependency exists.
 3. Thediagnostic method as recited in claim 1, wherein the third stepincludes: in a counting stage, determining a number of failure messagesappearing within the time window T; and in a timing stage, determining aduration of the failure messages.
 4. The diagnostic method as recited inclaim 1, wherein the generating includes performing a probabilisticanalysis if an ambiguity exists regarding a location of the failure inan additional step.
 5. The diagnostic method as recited in claim 4,wherein the probabilistic analysis includes taking breakdown messagesgenerated by equipment subassemblies, identifying the equipmentsubassemblies having ambiguities regarding the location of the failure,and determining a ratio of a mean time between failure (MTBF) of theequipment subassemblies containing additional information on which tobase the location of the failure.
 6. The diagnostic method as recited inclaim 1, wherein the fourth step includes determining a chronologicalorder of the failure messages in an ordering stage.
 7. The diagnosticmethod as recited in claim 1, wherein the fourth step includesperforming a context correlation analysis on the failure messages in acontext stage.
 8. The diagnostic method as recited in claim 1, whereinthe second step includes filtering the failure messages as a function ofa duration of an appearance of the failure message.
 9. The diagnosticmethod as recited in claim 1, wherein the second step includes filteringthe failure messages as a function of correlation between the failuremessages and one of an in-flight context and on the ground context in afilter-and-correlation stage.
 10. The diagnostic method as recited inclaim 1, wherein the second step includes filtering the failure messagesas a function of correlation of the failure messages with a data setincluding an appearance, a duration, a context, a generated failuremessage and additional data in another filter-and-correlation stage. 11.The diagnostic method as recited in claim 1, wherein the logical anddynamic relationships of the dynamic failure tree (DFT) include: a“NUMBER OF OCCURRENCES” gate that is true when a number of failuremessages recorded in flight is greater than or less than a definedthreshold; a “DURATION” gate that is true when a duration of appearanceof a failure message is greater than or less than a defined threshold; a“PAND” gate; and a “timed PAND” gate that is true when its inputs aretrue and appear in a determined order with a rate of appearance of abreakdown being greater than or less than a determined threshold.
 12. Adevice for assisting in locating a failure in a complex system byimplementing the diagnostic method of claim 1, the device comprising: adetector device disposed in the complex system configured to detect anddeliver operating information of the system; a central unit including astorage device configured to store predetermined data, a filter deviceconfigured to filter at least one of operating information andassociated failure messages, and a sorting, an analysis and a comparisondevice configured to generate a failure locating message based on thepredetermined data and the operating information; a presentation deviceconfigured to present the failure location message generated by thecentral unit; and a recording device incorporated in the central unitconfigured to store the operating information giving rise to the failuremessage and the failure location messages presented from thepresentation device.
 13. The device as recited in claim 12, wherein thecentral unit includes a device configured to define time window Tshorter than three seconds during which failure occurrences must appearin succession in order to be identified.
 14. An aircraft having a devicefor assisting in locating a failure in a complex system by implementingthe diagnostic method of claim 1, the device comprising: a detectordevice disposed in the complex system configured to detect and deliveroperating information of the system; a central unit including a storagedevice configured to store predetermined data, a filter deviceconfigured to filter at least one of operating information andassociated failure messages, and a sorting, an analysis and a comparisondevice configured to generate a failure locating message based on thepredetermined data and the operating information; a presentation deviceconfigured to present the failure location message generated by thecentral unit; and a recording device incorporated in the central unitconfigured to store the operating information giving rise to the failuremessage and the failure location messages presented from thepresentation device.